Robust Learning-Enabled Intelligence for the Internet of Things: A Survey From the Perspectives of Noisy Data and Adversarial Examples
Robust Learning-Enabled Intelligence for the Internet of Things: A Survey From the Perspectives of Noisy Data and Adversarial Examples
复制标题
面向物联网的健壮学习智能：从噪声数据和对抗性实例的角度综述
DOI：
10.1109/jiot.2020.3018691
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发表时间：
2021-06
影响因子：
10.6
通讯作者：
Yulei Wu
中科院分区：
文献类型：
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作者：
Yulei Wu
物联网（IoT）已被广泛应用于自动化、健康、能源和制造业等一系列垂直领域。这些领域的许多应用，如自动驾驶汽车和远程手术，都是关键和高风险的应用，需要先进的机器学习（ML）模型来进行数据分析。从本质上讲，大量物联网设备收集的训练和测试数据可能包含噪声（例如，异常数据，不正确的标签和不完整的信息）和对抗性示例。这需要机器学习模型的高鲁棒性，以便为物联网应用做出可靠的决策。近年来，鲁棒性机器学习的研究受到了学术界和工业界的极大关注。本文将研究能够实现物联网智能的高弹性和可靠性的强大ML模型的最新技术和代表性作品。鲁棒性的两个方面将被关注，即当ML模型的训练数据包含噪声和对抗性示例时，这可能通常发生在许多现实世界的物联网场景中。此外，还将研究神经网络和强化学习框架的可靠性。这两种机器学习范式都被广泛用于处理物联网场景中的数据。讨论潜在的研究挑战和有待解决的问题，为未来的研究方向提供建议。
The Internet of Things (IoT) has been widely adopted in a range of verticals, e.g., automation, health, energy, and manufacturing. Many of the applications in these sectors, such as self-driving cars and remote surgery, are critical and high stakes applications, calling for advanced machine learning (ML) models for data analytics. Essentially, the training and testing data that are collected by massive IoT devices may contain noise (e.g., abnormal data, incorrect labels, and incomplete information) and adversarial examples. This requires high robustness of ML models to make reliable decisions for IoT applications. The research of robust ML has received tremendous attention from both academia and industry in recent years. This article will investigate the state of the art and representative works of robust ML models that can enable high resilience and reliability of IoT intelligence. Two aspects of robustness will be focused on, i.e., when the training data of ML models contain noises and adversarial examples, which may typically happen in many real-world IoT scenarios. In addition, the reliability of both neural networks and reinforcement learning framework will be investigated. Both of these two ML paradigms have been widely used in handling data in IoT scenarios. The potential research challenges and open issues will be discussed to provide future research directions.